SoroushVahidi/scidocs huggingface dataset:
Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories
(2002)

The proliferation of information technology (IT) in supporting highly specialized tasks and services has made it increasingly important to understand the factors essential to technology acceptance by individuals. In a typical professional setting, the essential characteristics of user, technology, and context may differ considerably from those in ordinary business settings. This study examined physicians’ acceptance of telemedicine technology. Following a theory comparison approach, it evaluated the extent to which prevailing intention-based models, including the technology acceptance model (TAM), the theory of planned behavior (TPB) and an integrated model, could explain individual physicians’ technology acceptance decisions. Based on responses from more than 400 physicians, both models were evaluated in terms of overall fit, explanatory power, and their causal links. Overall, findings suggest that TAM may be more appropriate than TPB for examining technology acceptance by individual professionals and that the integrated model, although more fully depicting physicians’ technology acceptance, may not provide significant additional explanatory power. Also, instruments developed and repeatedly tested in prior studies involving conventional end-users and business managers may not be valid in professional settings. Several interesting implications are also discussed. # 2002 Elsevier Science B.V. All rights reserved.
A Review of Information Quality Research - Develop a Research Agenda
(2007)

Recognizing the substantial development of information quality research, this review article analyzes three major aspects of information quality research: information quality assessment, information quality management and contextual information quality. Information quality assessment is analyzed by three components: information quality problem, dimension and assessment methodology. Information quality management is analyzed from three perspectives: quality management, information management and knowledge management. Following an overview of contextual information quality, this article analyzes information quality research in the context of information system and decision making. The analyzing results reveal the potential research streams and current research limitations of information quality. Aiming at bridging the research gaps, we conclude by providing the research issues for future information quality research and implications for empirical applications.
A tutorial on support vector regression
(2004)

In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention somemodifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.
An introduction to hidden Markov models
(1986)

The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
A sample size calculator for SMART pilot studies
(2016)

In clinical practice, as well as in other areas where interventions are provided, a sequential individualized approach to treatment is often necessary, whereby each treatment is adapted based on the object ′s response. An adaptive intervention is a sequence of decision rules which formalizes the provision of treatment at critical decision points in the care of an individual. In order to inform the development of an adaptive intervention, scientists are increasingly interested in the use of sequential multiple assignment randomized trials (SMART), which is a type of multistage randomized trial where individuals are randomized repeatedly at critical decision points to a set treatment options. While there is great interest in the use of SMART and in the development of adaptive interventions, both are relatively new to themedical and behavioral sciences. As a result, many clinical researchers will first implement a SMART pilot study (i.e., a small-scale version of a SMART) to examine feasibility and acceptability considerations prior to conducting a full-scale SMART study. A primary aim of this paper is to introduce a new methodology to calculate minimal sample size necessary for conducting a SMART pilot.
A foundation for representing and querying moving objects
(2000)

Spatio-temporal databases deal with geometries changing over time. The goal of our work is to provide a DBMS data model and query language capable of handling such time-dependent geometries, including those changing continuously that describe moving objects. Two fundamental abstractions are moving point and moving region, describing objects for which only the time-dependent position, or position and extent, respectively, are of interest. We propose to present such time-dependent geometries as attribute data types with suitable operations, that is, to provide an abstract data type extension to a DBMS data model and query language. This paper presents a design of such a system of abstract data types. It turns out that besides the main types of interest, moving point and moving region, a relatively large number of auxiliary data types are needed. For example, one needs a line type to represent the projection of a moving point into the plane, or a “moving real” to represent the time-dependent distance of two points. It then becomes crucial to achieve (i) orthogonality in the design of the system, i.e., type constructors can be applied unifomly; (ii) genericity and consistency of operations, i.e., operations range over as many types as possible and behave consistently; and (iii) closure and consistency between structure and operations of nontemporal and related temporal types. Satisfying these goal leads to a simple and expressive system of abstract data types that may be integrated into a query language to yield a powerful language for querying spatio-temporal data, including moving objects. The paper formally defines the types and operations, offers detailed insight into the considerations that went into the design, and exemplifies the use of the abstract data types using SQL. The paper offers a precise and conceptually clean foundation for implementing a spatio-temporal DBMS extension.
Free Form based active contours for image segmentation and free space perception
(2016)

In this paper we present a novel approach for representing and evolving deformable active contours. The method combines piecewise regular Bézier models and curve evolution defined by local Free Form Deformation. The contour deformation is locally constrained which allows contour convergence with almost linear complexity while adapting to various shape settings and handling topology changes of the active contour. We demonstrate the effectiveness of the new active contour scheme for visual free space perception and segmentation using omnidirectional images acquired by a robot exploring unknown indoor and outdoor environments. Several experiments validate the approach with comparison to state-of-the art parametric and geometric active contours and provide fast and real-time robot free space segmentation and navigation.
Neon: A (Big) (Fast) Single-Chip 3D Workstation Graphics Accelerator
(1999)

High-performance 3D graphics accelerators traditionally require multiple chips on multiple boards. Specialized chips perform geometry transformations and lighting computations, rasterizing, pixel processing, and texture mapping. Multiple chip designs are often scalable: they can increase performance by using more chips. Scalability has obvious costs: a minimal configuration needs several chips, and some configurations must replicate texture maps. A less obvious cost is the almost irresistible temptation to replicate chips to increase performance, rather than to design individual chips for higher performance in the first place. In contrast, Neon is a single chip that performs like a multichip design. Neon accelerates OpenGL 3D rendering, as well as X11 and Windows/NT 2D rendering. Since our pin budget limited peak memory bandwidth, we designed Neon from the memory system upward in order to reduce bandwidth requirements. Neon has no special-purpose memories; its eight independent 32-bit memory controllers can access color buffers, Z depth buffers, stencil buffers, and texture data. To fit our gate budget, we shared logic among different operations with similar implementation requirements, and left floating point calculations to Digital's Alpha CPUs. Neon’s performance is between HP’s Visualize fx and fx, and is well above SGI’s MXE for most operations. Neon-based boards cost much less than these competitors, due to a small part count and use of commodity SDRAMs.
Boosting for transfer learning
(2007)

Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.
AutoBrief: an experimental system for the automatic generation of briefings in integrated text and information graphics
(2004)

This paper describes AutoBrief, an experimental intelligent multimedia presentation system that generates presentations in text and information graphics in the domain of transportation scheduling. Acting as an intelligent assistant, AutoBrief creates a presentation to communicate its analysis of alternative schedules. In addition, the multimedia presentation facilitates data exploration through its complex information visualizations and support for direct manipulation of presentation elements. AutoBrief’s research contributions include (1) a design enabling a new human–computer interaction style in which intelligent multimedia presentation objects (textual or graphic) can be used by the audience in direct manipulation operations for data exploration, (2) an application-independent approach to multimedia generation based on the representation of communicative goals suitable for both generation of text and of complex information graphics, and (3) an application-independent approach to intelligent graphic design based upon communicative goals. This retrospective overview paper, aimed at a multidisciplinary audience from the fields of human–computer ARTICLE IN PRESS *Corresponding author. Tel.: +1-336-2561133; fax: +1-336-3345949. E-mail addresses: nlgreen@uncg.edu (N.L. Green), carenini@cs.ubc.ca (G. Carenini), kerpedjiev@maya.com (S. Kerpedjiev), mattis@mayaviz.com (J. Mattis), j.moore@ed.ac.uk (J.D. Moore), roth+@cs.cmu.edu (S.F. Roth). 1071-5819/$ see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2003.10.007 interaction and natural language generation, presents AutoBrief’s design and design rationale. r 2003 Elsevier Ltd. All rights reserved.
Failure Analysis of SGI XFS File System
(2005)

Commodity file systems expect a fail stop disk. But todays disks fail in unexpected ways. Disks exhibit latent sector errors, silent corruption and transient failures to name a few. In this paper we study the behavior of SGI XFS to such errors. File systems play a key role in handling most data and hence the failure handling policy of the file system plays a major role in ensuring the integrity of the data. XFS is a highly scalable journaling file system developed by SGI. We analyze the failure handling policy of XFS file system. We fail reads and writes of various blocks that originate from the file system and analyze the file system behavior. Some such blocks include super block, journal header block, commit block, data block, index blocks. We classify our errors according to the extended IRON Taxonomy. We see that XFS is vulnerable to these failures and does not possess a uniform failure handling policy. Further the file system at times silently corrupts data. We also see that XFS has very little internal redundancy even for critical structures such as the super block and B+Tree root. Finally we see that XFS, like most commodity file systems, fails to recover from these failures putting the data at risk.
The Wnt signaling pathway in development and disease.
(2004)

Tight control of cell-cell communication is essential for the generation of a normally patterned embryo. A critical mediator of key cell-cell signaling events during embryogenesis is the highly conserved Wnt family of secreted proteins. Recent biochemical and genetic analyses have greatly enriched our understanding of how Wnts signal, and the list of canonical Wnt signaling components has exploded. The data reveal that multiple extracellular, cytoplasmic, and nuclear regulators intricately modulate Wnt signaling levels. In addition, receptor-ligand specificity and feedback loops help to determine Wnt signaling outputs. Wnts are required for adult tissue maintenance, and perturbations in Wnt signaling promote both human degenerative diseases and cancer. The next few years are likely to see novel therapeutic reagents aimed at controlling Wnt signaling in order to alleviate these conditions.
Magnetic Optimization Algorithms a new synthesis
(2008)

A novel optimization algorithm is proposed here that is inspired by the principles of magnetic field theory. In the proposed Magnetic Optimization Algorithm (MOA) the possible solutions are magnetic particles scattered in the search space. Each magnetic particle has a measure of mass and magnetic field according to its fitness. The fitter magnetic particles are those with higher magnetic field and higher mass. These particles are located in a lattice-like environment and apply a force of attraction to their neighbors. The proposed cellular structure allows a better exploitation of local neighborhoods before they move towards the global best, hence it increases population diversity. Experimental results on 14 numerical benchmark functions show that MOA in some benchmark functions can work better than GA and PSO.
Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code
(2015)

Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.
Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies
(2017)

The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.
Automated Verification of Electrum Wallet
(2016)

We introduce a formal modeling in ASLan++ of the twofactor authentication protocol used by the Electrum Bitcoin wallet. This allows us to perform an automatic analysis of the wallet and show that it is secure for standard scenarios in Dolev Yao model [Dolev 1981]. The result could be derived thanks to some advanced features of the protocol analyzer such as the possibility to specify i) new intruder deduction rules with clauses and ii) non-deducibility constraints.
A Semi-Supervised Approach for Kernel-Based Temporal Clustering
(2015)

Temporal clustering refers to the partitioning of a time series into multiple nonoverlapping segments that belong to k temporal clusters, in such a way that segments in the same cluster are more similar to each other than to those in other clusters. Temporal clustering is a fundamental task in many fields, such as computer animation, computer vision, health care, and robotics. The applications of temporal clustering in those areas are diverse, and include human-motion imitation and recognition, emotion analysis, human activity segmentation, automated rehabilitation exercise analysis, and human-computer interaction. However, temporal clustering using a completely unsupervised method may not produce satisfactory results. Similar to regular clustering, temporal clustering also benefits from some expert knowledge that may be available. The type of approach that utilizes a small amount of knowledge to “guide” the clustering process is known as “semi-supervised clustering.” Semi-supervised temporal clustering is a strategy in which extra knowledge, in the form of pairwise constraints, is incorporated into the temporal data to help with the partitioning problem. This thesis proposes a process to adapt and transform two kernel-based methods into semi-supervised temporal clustering methods. The proposed process is exclusive to kernel-based clustering methods, and is based on two concepts. First, it uses the idea of instance-level constraints, in the form of must-link and cannot-link, to supervise the clustering methods. Second, it uses a dynamic-programming method to search for the optimal temporal clusters. The proposed process is applied to two algorithms, aligned cluster analysis (ACA) and spectral clustering. To validate the advantages of the proposed temporal semi-supervised clustering methods, a comparative analysis was performed, using the original versions of the algorithm and another semi-supervised temporal cluster. This evaluation was conducted with both synthetic data and two real-world applications. The first application includes two naturalistic audio-visual human emotion datasets, and the second application focuses on human-motion segmentation. Results show substantial improvements in accuracy, with minimal supervision, compared to unsupervised and other temporal semi-supervised approaches, without compromising time performance.
BRAINIAC: Bringing reliable accuracy into neurally-implemented approximate computing
(2015)

Applications with large amounts of data, real-time constraints, ultra-low power requirements, and heavy computational complexity present significant challenges for modern computing systems, and often fall within the category of high performance computing (HPC). As such, computer architects have looked to high performance single instruction multiple data (SIMD) architectures, such as accelerator-rich platforms, for handling these workloads. However, since the results of these applications do not always require exact precision, approximate computing may also be leveraged. In this work, we introduce BRAINIAC, a heterogeneous platform that combines precise accelerators with neural-network-based approximate accelerators. These reconfigurable accelerators are leveraged in a multi-stage flow that begins with simple approximations and resorts to more complex ones as needed. We employ high-level, application-specific light-weight checks (LWCs) to throttle this multi-stage acceleration flow and reliably ensure user-specified accuracy at runtime. Evaluation of the performance and energy of our heterogeneous platform for error tolerance thresholds of 5%-25% demonstrates an average of 3× gain over computation that only includes precise acceleration, and 15×-35× gain over software-based computation.